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Prompt engineering for text generation: Difference between revisions
Prompt engineering for text generation (view source)
Revision as of 23:46, 7 April 2023
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====Embeddings via Contrastive Learning==== | ====Embeddings via Contrastive Learning==== | ||
Rubin et al. (2022) suggested training embeddings through [[contrastive learning]] specific to one [[training dataset]] for in-context learning sample selection. This approach measures the quality of an example based on a conditioned probability assigned by the language model.<ref name="”114”">Rubin et al. (2022) Learning To Retrieve Prompts for In-Context Learning https://arxiv.org/abs/2112.08633</ref> | Rubin et al. (2022) suggested training embeddings through [[contrastive learning]] specific to one [[training dataset]] for [[in-context learning]] sample selection. This approach measures the quality of an example based on a conditioned probability assigned by the language model.<ref name="”114”">Rubin et al. (2022) Learning To Retrieve Prompts for In-Context Learning https://arxiv.org/abs/2112.08633</ref> | ||
====Q-Learning==== | ====Q-Learning==== | ||